Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach.
By Jaideep Pathak, Brian R. Hunt, Michelle Girvan, Zhixin Lu, Edward Ott. Physical Review Letters, 2018
1,176 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/65b6c541ac4f1b57382fb4f95fe3cf4ccc8752be
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Petros Koumoutsakos
- Steven L. Brunton
- Bernd R. Noack
- Themistoklis P. Sapsis
- Pantelis R. Vlachas
- Michelle Girvan
- Jaideep Pathak
- Ricardo Vinuesa
In Statistical and Nonlinear Physics
- Steven L. Brunton
- Petros Koumoutsakos
- Bernd R. Noack
- Ricardo Vinuesa
- J. Nathan Kutz
- Themistoklis P. Sapsis
- Pantelis R. Vlachas
- Michelle Girvan
Citations in the corpus
- Machine Learning for Fluid Mechanics (2019, methodology): “The CMA-ES is closely related to a number of other algorithms including the mixed Bayesian optimization algorithms (MBOAs) (Pelikan et al. 2004), and the reader is referred to Kern et al. (2004) for a comparative review.”
- Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics (2019, methodology): “In this work, a large RC with d h = 9000 nodes is employed for LS calculation in the Kuramoto– Sivashinsky equation with parameter L = 60 and D = 128 grid points as in Pathak, Hunt et al. (2018).”
- Emerging opportunities and challenges for the future of reservoir computing (2024, positive): “Accompanied with a nonlinear readout function, the RC network can learn and evolve Kuramoto-Sivashinsky (KS) equation relatively accurate up to a time length of around 5 Lyapunov times 21 .”
- Machine learning and the physical sciences (2019, background): “For instance (Pathak et al., 2018, 2017) used recurrent neural networks called an echo state networks or reservoir computers (Jaeger and Haas, 2004) to predict the trajectories of a chaotic dynamical system and of models used for weather prediction.”
- Enhancing computational fluid dynamics with machine learning (2021, background): “Several neural networks are capable of learning nonlinear dynamics [109], including the LSTM network [110–112] and echo-state networks [113], which are a form of reservoir computing.”
- Machine learning and applications in ultrafast photonics (2020, background): “As research continues to progress both in the development of machine-learning algorithms and ultrafast photonics technologies, we can expect even more fruitful interactions with increased influence of the former in the physical understanding, design, optimization and operation of the latter.”